To use incense we first have to instantiate an experiment loader that will enable us to query the database for specific runs.
| targets_type | iteration | autoencoder_type | batch_size | artifacts | |
|---|---|---|---|---|---|
| exp_id | |||||
| 58 | Mnist | False | Over_dim_tied_iteration | 256 | {'history_autoencoder_iteration': Artifact(nam... |
| 59 | Mnist | False | Over_dim_tied_iteration | 128 | {'history_autoencoder_iteration': Artifact(nam... |
| 60 | Mnist | False | Over_dim_tied_iteration | 64 | {'history_autoencoder_iteration': Artifact(nam... |
| 61 | Mnist | False | Over_dim_tied_iteration | 32 | {'history_autoencoder_iteration': Artifact(nam... |
| 62 | 10_Targets | False | Over_dim_tied_iteration | 256 | {'history_autoencoder_iteration': Artifact(nam... |
| 63 | 10_Targets | False | Over_dim_tied_iteration | 128 | {'history_autoencoder_iteration': Artifact(nam... |
| 64 | 10_Targets | False | Over_dim_tied_iteration | 64 | {'history_autoencoder_iteration': Artifact(nam... |
| 65 | 10_Targets | False | Over_dim_tied_iteration | 32 | {'history_autoencoder_iteration': Artifact(nam... |
| targets_type | iteration | autoencoder_type | batch_size | artifacts | sort | |
|---|---|---|---|---|---|---|
| exp_id | ||||||
| 62 | 10_Targets | False | Over_dim_tied_iteration | 256 | {'history_autoencoder_iteration': Artifact(nam... | 0 |
| 63 | 10_Targets | False | Over_dim_tied_iteration | 128 | {'history_autoencoder_iteration': Artifact(nam... | 1 |
| 64 | 10_Targets | False | Over_dim_tied_iteration | 64 | {'history_autoencoder_iteration': Artifact(nam... | 2 |
| 65 | 10_Targets | False | Over_dim_tied_iteration | 32 | {'history_autoencoder_iteration': Artifact(nam... | 3 |
| 58 | Mnist | False | Over_dim_tied_iteration | 256 | {'history_autoencoder_iteration': Artifact(nam... | 4 |
| 59 | Mnist | False | Over_dim_tied_iteration | 128 | {'history_autoencoder_iteration': Artifact(nam... | 5 |
| 60 | Mnist | False | Over_dim_tied_iteration | 64 | {'history_autoencoder_iteration': Artifact(nam... | 6 |
| 61 | Mnist | False | Over_dim_tied_iteration | 32 | {'history_autoencoder_iteration': Artifact(nam... | 7 |
Red best overall, and also best of subset. Bes means for accuracy max, rest min. Green best of subset.
predictions_df_0
| Over_dim_tied_iteration 256 10_Targets | Over_dim_tied_iteration 128 10_Targets | Over_dim_tied_iteration 64 10_Targets | Over_dim_tied_iteration 32 10_Targets | Over_dim_tied_iteration 256 Mnist | Over_dim_tied_iteration 128 Mnist | Over_dim_tied_iteration 64 Mnist | Over_dim_tied_iteration 32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.9751 | 0.9602 | 0.5022 | 0.8382 | 0.9777 | 0.9781 | 0.976 | 0.3537 |
| 1 | 0.902 | 0.8754 | 0.1135 | 0.5707 | 0.9758 | 0.977 | 0.9758 | 0.1274 |
| 2 | 0.8328 | 0.8082 | 0.1135 | 0.5289 | 0.9702 | 0.9733 | 0.9732 | 0.1039 |
| 3 | 0.7978 | 0.7682 | 0.1135 | 0.5163 | 0.9637 | 0.9667 | 0.9673 | 0.1028 |
| 4 | 0.7785 | 0.7394 | 0.1135 | 0.5112 | 0.9574 | 0.9577 | 0.9611 | 0.1028 |
| 5 | 0.769 | 0.7212 | 0.1135 | 0.5098 | 0.9476 | 0.948 | 0.9528 | 0.1028 |
| 6 | 0.7617 | 0.7068 | 0.1135 | 0.5083 | 0.936 | 0.9332 | 0.9395 | 0.1028 |
| 7 | 0.758 | 0.6969 | 0.1135 | 0.5079 | 0.92 | 0.915 | 0.9253 | 0.1028 |
| Over_dim_tied_iteration 256 10_Targets | Over_dim_tied_iteration 128 10_Targets | Over_dim_tied_iteration 64 10_Targets | Over_dim_tied_iteration 32 10_Targets | Over_dim_tied_iteration 256 Mnist | Over_dim_tied_iteration 128 Mnist | Over_dim_tied_iteration 64 Mnist | Over_dim_tied_iteration 32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.365515 | 0.344711 | 0.455832 | 0.299053 | 0.0157922 | 0.0158386 | 0.0141918 | 0.381717 |
| 1 | 0.383135 | 29265.5 | 0.455832 | 162675 | 0.0267629 | 0.0275521 | 0.0228369 | 0.382225 |
| 2 | 0.393784 | 1.41281e+13 | 0.455832 | 3.33656e+12 | 0.0408471 | 0.0424907 | 0.0340128 | 0.382359 |
| 3 | 0.399944 | 6.82077e+21 | 0.455832 | 6.84377e+19 | 0.0568863 | 4.56198e+07 | 0.0468889 | 0.382421 |
| 4 | 0.40428 | 3.29294e+30 | 0.455832 | 1.40376e+27 | 0.0741922 | 2.28454e+24 | 35218.1 | 0.382439 |
| 5 | 0.407509 | inf | 0.455832 | inf | 7.43304e+07 | inf | 2.11025e+21 | 0.382443 |
| 6 | 0.409831 | inf | 0.455832 | inf | 3.47823e+23 | inf | inf | 0.382444 |
| 7 | 0.411346 | inf | 0.455832 | inf | inf | inf | inf | 0.382445 |
| Over_dim_tied_iteration 256 10_Targets | Over_dim_tied_iteration 128 10_Targets | Over_dim_tied_iteration 64 10_Targets | Over_dim_tied_iteration 32 10_Targets | Over_dim_tied_iteration 256 Mnist | Over_dim_tied_iteration 128 Mnist | Over_dim_tied_iteration 64 Mnist | Over_dim_tied_iteration 32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.258537 | 0.25949 | 0.265029 | 0.25146 | 0.0418313 | 0.0421456 | 0.0404086 | 0.264885 |
| 1 | 0.262569 | 1.75583 | 0.265029 | 4.08223 | 0.0533651 | 0.0544632 | 0.0503333 | 0.2652 |
| 2 | 0.264747 | 32754.5 | 0.265029 | 17316 | 0.0654345 | 0.0671523 | 0.0608999 | 0.265247 |
| 3 | 0.266198 | 7.19687e+08 | 0.265029 | 7.8422e+07 | 0.0772814 | 69.7403 | 0.0713959 | 0.265267 |
| 4 | 0.26706 | 1.58132e+13 | 0.265029 | 3.5517e+11 | 0.08871 | 1.55883e+10 | 2.42887 | 0.265273 |
| 5 | 0.267646 | 3.47451e+17 | 0.265029 | 1.60855e+15 | 184.664 | 3.48837e+18 | 5.74526e+08 | 0.265274 |
| 6 | 0.26807 | 7.63429e+21 | 0.265029 | 7.28505e+18 | 1.26258e+10 | 7.80632e+26 | 1.40645e+17 | 0.265274 |
| 7 | 0.268339 | 1.67743e+26 | 0.265029 | 3.29937e+22 | 8.63687e+17 | inf | 3.44299e+25 | 0.265275 |
predictions_df_10
| Over_dim_tied_iteration 256 10_Targets | Over_dim_tied_iteration 128 10_Targets | Over_dim_tied_iteration 64 10_Targets | Over_dim_tied_iteration 32 10_Targets | Over_dim_tied_iteration 256 Mnist | Over_dim_tied_iteration 128 Mnist | Over_dim_tied_iteration 64 Mnist | Over_dim_tied_iteration 32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.9646 | 0.9512 | 0.4513 | 0.8199 | 0.9516 | 0.9557 | 0.9548 | 0.3043 |
| 1 | 0.8851 | 0.8603 | 0.1135 | 0.5655 | 0.9596 | 0.9657 | 0.9612 | 0.1161 |
| 2 | 0.8078 | 0.791 | 0.1135 | 0.5198 | 0.9578 | 0.9631 | 0.9588 | 0.1032 |
| 3 | 0.7755 | 0.7519 | 0.1135 | 0.5035 | 0.9498 | 0.9551 | 0.9544 | 0.1028 |
| 4 | 0.7555 | 0.7218 | 0.1135 | 0.4978 | 0.9406 | 0.944 | 0.9458 | 0.1028 |
| 5 | 0.7442 | 0.7023 | 0.1135 | 0.4967 | 0.9283 | 0.9317 | 0.9341 | 0.1028 |
| 6 | 0.7376 | 0.6904 | 0.1135 | 0.4959 | 0.9095 | 0.9137 | 0.9174 | 0.1028 |
| 7 | 0.7331 | 0.6816 | 0.1135 | 0.4956 | 0.8913 | 0.8937 | 0.8983 | 0.1028 |
| Over_dim_tied_iteration 256 10_Targets | Over_dim_tied_iteration 128 10_Targets | Over_dim_tied_iteration 64 10_Targets | Over_dim_tied_iteration 32 10_Targets | Over_dim_tied_iteration 256 Mnist | Over_dim_tied_iteration 128 Mnist | Over_dim_tied_iteration 64 Mnist | Over_dim_tied_iteration 32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.36237 | 0.341752 | 0.455832 | 0.28745 | 0.0423389 | 7.8254e+06 | 5.28285e+07 | 0.381441 |
| 1 | 0.3818 | 0.369189 | 0.455832 | 0.344956 | 0.0498952 | 3.91871e+23 | 3.16585e+24 | 0.382245 |
| 2 | 0.394055 | 0.386584 | 0.455832 | 37766.9 | 0.0625914 | inf | inf | 0.382367 |
| 3 | 0.400722 | 0.401297 | 0.455832 | 7.74555e+11 | 0.0779077 | inf | inf | 0.382424 |
| 4 | 0.405389 | 0.414306 | 0.455832 | 1.58873e+19 | 1.8792e+08 | inf | inf | 0.382439 |
| 5 | 0.408513 | 0.423468 | 0.455832 | 3.25871e+26 | 8.79355e+23 | nan | nan | 0.382443 |
| 6 | 0.410958 | 0.430074 | 0.455832 | inf | inf | nan | nan | 0.382444 |
| 7 | 0.412752 | 0.434429 | 0.455832 | inf | inf | nan | nan | 0.382445 |
| Over_dim_tied_iteration 256 10_Targets | Over_dim_tied_iteration 128 10_Targets | Over_dim_tied_iteration 64 10_Targets | Over_dim_tied_iteration 32 10_Targets | Over_dim_tied_iteration 256 Mnist | Over_dim_tied_iteration 128 Mnist | Over_dim_tied_iteration 64 Mnist | Over_dim_tied_iteration 32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.258278 | 0.259856 | 0.265029 | 0.252088 | 0.0697001 | 63.3859 | 279.488 | 0.264782 |
| 1 | 0.262572 | 0.265991 | 0.265029 | 0.26107 | 0.0737914 | 1.41672e+10 | 6.84034e+10 | 0.265172 |
| 2 | 0.265109 | 0.271233 | 0.265029 | 1.96198 | 0.0822621 | 3.17035e+18 | 1.67452e+19 | 0.265238 |
| 3 | 0.266622 | 0.275797 | 0.265029 | 7673.86 | 0.091958 | 7.09464e+26 | 4.09924e+27 | 0.265265 |
| 4 | 0.267631 | 0.27967 | 0.265029 | 3.47534e+07 | 156.684 | inf | inf | 0.265272 |
| 5 | 0.26822 | 0.282567 | 0.265029 | 1.57397e+11 | 1.07114e+10 | nan | nan | 0.265274 |
| 6 | 0.268671 | 0.284722 | 0.265029 | 7.12843e+14 | 7.32726e+17 | nan | nan | 0.265274 |
| 7 | 0.269024 | 0.286244 | 0.265029 | 3.22844e+18 | 5.01231e+25 | nan | nan | 0.265275 |
predictions_df_20
| Over_dim_tied_iteration 256 10_Targets | Over_dim_tied_iteration 128 10_Targets | Over_dim_tied_iteration 64 10_Targets | Over_dim_tied_iteration 32 10_Targets | Over_dim_tied_iteration 256 Mnist | Over_dim_tied_iteration 128 Mnist | Over_dim_tied_iteration 64 Mnist | Over_dim_tied_iteration 32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.9528 | 0.9404 | 0.355 | 0.7929 | 0.9148 | 0.916 | 0.919 | 0.2611 |
| 1 | 0.8598 | 0.8416 | 0.1135 | 0.5634 | 0.9348 | 0.9416 | 0.9354 | 0.109 |
| 2 | 0.7796 | 0.774 | 0.1135 | 0.5066 | 0.9308 | 0.9398 | 0.9347 | 0.1029 |
| 3 | 0.7458 | 0.7335 | 0.1135 | 0.4882 | 0.9196 | 0.933 | 0.9272 | 0.1028 |
| 4 | 0.7292 | 0.7042 | 0.1135 | 0.4832 | 0.906 | 0.9201 | 0.9131 | 0.1028 |
| 5 | 0.7168 | 0.6887 | 0.1135 | 0.4809 | 0.8891 | 0.9001 | 0.8942 | 0.1028 |
| 6 | 0.7099 | 0.6759 | 0.1135 | 0.4802 | 0.8677 | 0.8817 | 0.8751 | 0.1028 |
| 7 | 0.7049 | 0.6685 | 0.1135 | 0.48 | 0.8438 | 0.8591 | 0.8544 | 0.1028 |
| Over_dim_tied_iteration 256 10_Targets | Over_dim_tied_iteration 128 10_Targets | Over_dim_tied_iteration 64 10_Targets | Over_dim_tied_iteration 32 10_Targets | Over_dim_tied_iteration 256 Mnist | Over_dim_tied_iteration 128 Mnist | Over_dim_tied_iteration 64 Mnist | Over_dim_tied_iteration 32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.359457 | 0.338906 | 0.455832 | 0.28566 | 0.0659356 | 1.17554e+08 | 3.607e+08 | 0.381203 |
| 1 | 0.381086 | 0.368372 | 0.455832 | 0.342205 | 0.0720645 | 5.88677e+24 | 2.16157e+25 | 0.382256 |
| 2 | 0.394452 | 0.387029 | 0.455832 | 0.382495 | 9.61913e+07 | inf | inf | 0.382374 |
| 3 | 0.401379 | 0.403141 | 0.455832 | 0.404541 | 4.50119e+23 | inf | inf | 0.382425 |
| 4 | 0.406072 | 0.417865 | 0.455832 | 0.415999 | inf | inf | nan | 0.38244 |
| 5 | 0.409475 | 0.428316 | 0.455832 | 0.421818 | inf | nan | nan | 0.382443 |
| 6 | 0.411821 | 0.435205 | 0.455832 | 0.424462 | inf | nan | nan | 0.382444 |
| 7 | 0.413506 | 0.439611 | 0.455832 | 0.42568 | nan | nan | nan | 0.382445 |
| Over_dim_tied_iteration 256 10_Targets | Over_dim_tied_iteration 128 10_Targets | Over_dim_tied_iteration 64 10_Targets | Over_dim_tied_iteration 32 10_Targets | Over_dim_tied_iteration 256 Mnist | Over_dim_tied_iteration 128 Mnist | Over_dim_tied_iteration 64 Mnist | Over_dim_tied_iteration 32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.258337 | 0.260514 | 0.265029 | 0.253837 | 0.0891158 | 475.75 | 1120.34 | 0.264649 |
| 1 | 0.262999 | 0.267079 | 0.265029 | 0.262599 | 0.0905341 | 1.06435e+11 | 2.74241e+11 | 0.265137 |
| 2 | 0.265636 | 0.272725 | 0.265029 | 0.270225 | 120.947 | 2.38182e+19 | 6.71344e+19 | 0.26523 |
| 3 | 0.267001 | 0.277685 | 0.265029 | 0.274898 | 8.26698e+09 | 5.33005e+27 | 1.64345e+28 | 0.265263 |
| 4 | 0.267922 | 0.282041 | 0.265029 | 0.277418 | 5.65513e+17 | inf | nan | 0.265271 |
| 5 | 0.268517 | 0.285193 | 0.265029 | 0.278654 | 3.86846e+25 | nan | nan | 0.265274 |
| 6 | 0.268887 | 0.28735 | 0.265029 | 0.279223 | inf | nan | nan | 0.265274 |
| 7 | 0.26921 | 0.288814 | 0.265029 | 0.279467 | nan | nan | nan | 0.265275 |
predictions_df_30
| Over_dim_tied_iteration 256 10_Targets | Over_dim_tied_iteration 128 10_Targets | Over_dim_tied_iteration 64 10_Targets | Over_dim_tied_iteration 32 10_Targets | Over_dim_tied_iteration 256 Mnist | Over_dim_tied_iteration 128 Mnist | Over_dim_tied_iteration 64 Mnist | Over_dim_tied_iteration 32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.9307 | 0.9116 | 0.2649 | 0.7527 | 0.8689 | 0.8663 | 0.8812 | 0.2199 |
| 1 | 0.826 | 0.8137 | 0.1135 | 0.545 | 0.8978 | 0.9068 | 0.9065 | 0.1063 |
| 2 | 0.7485 | 0.7446 | 0.1135 | 0.4916 | 0.8942 | 0.9058 | 0.905 | 0.103 |
| 3 | 0.7196 | 0.7078 | 0.1135 | 0.4696 | 0.881 | 0.8967 | 0.8958 | 0.1028 |
| 4 | 0.7031 | 0.6798 | 0.1135 | 0.4621 | 0.8632 | 0.8823 | 0.8799 | 0.1028 |
| 5 | 0.6911 | 0.6629 | 0.1135 | 0.4601 | 0.8401 | 0.8632 | 0.8599 | 0.1028 |
| 6 | 0.684 | 0.652 | 0.1135 | 0.4599 | 0.8145 | 0.8415 | 0.8374 | 0.1028 |
| 7 | 0.6787 | 0.6455 | 0.1135 | 0.46 | 0.7898 | 0.818 | 0.8139 | 0.1028 |
| Over_dim_tied_iteration 256 10_Targets | Over_dim_tied_iteration 128 10_Targets | Over_dim_tied_iteration 64 10_Targets | Over_dim_tied_iteration 32 10_Targets | Over_dim_tied_iteration 256 Mnist | Over_dim_tied_iteration 128 Mnist | Over_dim_tied_iteration 64 Mnist | Over_dim_tied_iteration 32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.356639 | 0.336023 | 0.455832 | 0.284259 | 4.21162e+06 | 3.22352e+08 | 7.00259e+08 | 0.380676 |
| 1 | 0.382153 | 0.368424 | 0.455832 | 0.342949 | 1.97078e+22 | 1.61426e+25 | 4.19645e+25 | 0.382182 |
| 2 | 0.39611 | 0.389777 | 0.455832 | 0.385501 | inf | inf | inf | 0.382354 |
| 3 | 0.403344 | 0.407545 | 0.455832 | 0.409722 | inf | inf | inf | 0.382421 |
| 4 | 0.408102 | 0.423431 | 0.455832 | 0.422368 | inf | nan | nan | 0.382439 |
| 5 | 0.411636 | 0.434447 | 0.455832 | 0.428781 | nan | nan | nan | 0.382443 |
| 6 | 0.413949 | 0.4415 | 0.455832 | 0.431519 | nan | nan | nan | 0.382444 |
| 7 | 0.415559 | 0.445994 | 0.455832 | 0.432734 | nan | nan | nan | 0.382445 |
| Over_dim_tied_iteration 256 10_Targets | Over_dim_tied_iteration 128 10_Targets | Over_dim_tied_iteration 64 10_Targets | Over_dim_tied_iteration 32 10_Targets | Over_dim_tied_iteration 256 Mnist | Over_dim_tied_iteration 128 Mnist | Over_dim_tied_iteration 64 Mnist | Over_dim_tied_iteration 32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.259134 | 0.261901 | 0.265029 | 0.255998 | 56.2033 | 1053.86 | 1837.66 | 0.264363 |
| 1 | 0.264191 | 0.269075 | 0.265029 | 0.264821 | 3.83789e+09 | 2.35793e+11 | 4.49837e+11 | 0.265057 |
| 2 | 0.266636 | 0.27537 | 0.265029 | 0.273113 | 2.62536e+17 | 5.2766e+19 | 1.10121e+20 | 0.265209 |
| 3 | 0.268021 | 0.280764 | 0.265029 | 0.278283 | 1.79591e+25 | 1.1808e+28 | 2.69576e+28 | 0.265258 |
| 4 | 0.268939 | 0.285382 | 0.265029 | 0.281038 | inf | nan | nan | 0.26527 |
| 5 | 0.269575 | 0.288599 | 0.265029 | 0.282385 | nan | nan | nan | 0.265273 |
| 6 | 0.26996 | 0.290792 | 0.265029 | 0.282928 | nan | nan | nan | 0.265274 |
| 7 | 0.270223 | 0.292267 | 0.265029 | 0.283154 | nan | nan | nan | 0.265274 |
predictions_df_40
| Over_dim_tied_iteration 256 10_Targets | Over_dim_tied_iteration 128 10_Targets | Over_dim_tied_iteration 64 10_Targets | Over_dim_tied_iteration 32 10_Targets | Over_dim_tied_iteration 256 Mnist | Over_dim_tied_iteration 128 Mnist | Over_dim_tied_iteration 64 Mnist | Over_dim_tied_iteration 32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.8902 | 0.8749 | 0.2143 | 0.703 | 0.8192 | 0.8142 | 0.8248 | 0.184 |
| 1 | 0.7871 | 0.774 | 0.1135 | 0.529 | 0.85 | 0.8549 | 0.854 | 0.1053 |
| 2 | 0.7104 | 0.71 | 0.1135 | 0.4747 | 0.846 | 0.8597 | 0.8552 | 0.1029 |
| 3 | 0.6767 | 0.6753 | 0.1135 | 0.4573 | 0.8322 | 0.8526 | 0.8444 | 0.1029 |
| 4 | 0.6578 | 0.648 | 0.1135 | 0.4499 | 0.8129 | 0.838 | 0.8277 | 0.1029 |
| 5 | 0.6451 | 0.6311 | 0.1135 | 0.4468 | 0.7902 | 0.817 | 0.8072 | 0.1029 |
| 6 | 0.6396 | 0.6204 | 0.1135 | 0.4464 | 0.7655 | 0.7925 | 0.7847 | 0.1029 |
| 7 | 0.6354 | 0.6134 | 0.1135 | 0.4463 | 0.7358 | 0.765 | 0.7602 | 0.1029 |
| Over_dim_tied_iteration 256 10_Targets | Over_dim_tied_iteration 128 10_Targets | Over_dim_tied_iteration 64 10_Targets | Over_dim_tied_iteration 32 10_Targets | Over_dim_tied_iteration 256 Mnist | Over_dim_tied_iteration 128 Mnist | Over_dim_tied_iteration 64 Mnist | Over_dim_tied_iteration 32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.352247 | 0.333506 | 0.455832 | 0.28391 | 2.23681e+07 | 7.50971e+08 | 8.40472e+08 | 0.383479 |
| 1 | 0.381138 | 0.369243 | 0.455832 | 0.345825 | 1.04669e+23 | 3.76068e+25 | 5.03671e+25 | 8.83669 |
| 2 | 0.396389 | 0.393616 | 0.455832 | 0.39216 | inf | inf | inf | 8.83691 |
| 3 | 0.404912 | 0.413641 | 0.455832 | 0.417924 | inf | inf | inf | 8.83699 |
| 4 | 0.410318 | 0.430681 | 0.455832 | 0.431377 | inf | nan | nan | 8.83701 |
| 5 | 0.413839 | 0.442381 | 0.455832 | 0.438145 | nan | nan | nan | 8.83701 |
| 6 | 0.416148 | 0.449849 | 0.455832 | 0.441277 | nan | nan | nan | 8.83702 |
| 7 | 0.417803 | 0.454675 | 0.455832 | 0.442651 | nan | nan | nan | 8.83702 |
| Over_dim_tied_iteration 256 10_Targets | Over_dim_tied_iteration 128 10_Targets | Over_dim_tied_iteration 64 10_Targets | Over_dim_tied_iteration 32 10_Targets | Over_dim_tied_iteration 256 Mnist | Over_dim_tied_iteration 128 Mnist | Over_dim_tied_iteration 64 Mnist | Over_dim_tied_iteration 32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.259634 | 0.263801 | 0.265029 | 0.258862 | 159.226 | 1841.29 | 2046.98 | 0.264629 |
| 1 | 0.264975 | 0.271778 | 0.265029 | 0.268088 | 1.08848e+10 | 4.11998e+11 | 5.01075e+11 | 0.300326 |
| 2 | 0.267553 | 0.278895 | 0.265029 | 0.277158 | 7.44592e+17 | 9.21972e+19 | 1.22664e+20 | 0.30054 |
| 3 | 0.269104 | 0.284811 | 0.265029 | 0.282745 | 5.09347e+25 | 2.0632e+28 | 3.00281e+28 | 0.300606 |
| 4 | 0.270094 | 0.289681 | 0.265029 | 0.28577 | inf | nan | nan | 0.300624 |
| 5 | 0.270682 | 0.293102 | 0.265029 | 0.28724 | nan | nan | nan | 0.300628 |
| 6 | 0.271038 | 0.295354 | 0.265029 | 0.287879 | nan | nan | nan | 0.300629 |
| 7 | 0.271325 | 0.296904 | 0.265029 | 0.288151 | nan | nan | nan | 0.300629 |
predictions_df_50
| Over_dim_tied_iteration 256 10_Targets | Over_dim_tied_iteration 128 10_Targets | Over_dim_tied_iteration 64 10_Targets | Over_dim_tied_iteration 32 10_Targets | Over_dim_tied_iteration 256 Mnist | Over_dim_tied_iteration 128 Mnist | Over_dim_tied_iteration 64 Mnist | Over_dim_tied_iteration 32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.8446 | 0.8315 | 0.1916 | 0.6435 | 0.762 | 0.7483 | 0.7689 | 0.1408 |
| 1 | 0.7456 | 0.718 | 0.1135 | 0.5062 | 0.7905 | 0.798 | 0.803 | 0.1064 |
| 2 | 0.6764 | 0.6505 | 0.1135 | 0.4524 | 0.7901 | 0.8043 | 0.8049 | 0.1031 |
| 3 | 0.6486 | 0.6164 | 0.1135 | 0.4315 | 0.7788 | 0.7927 | 0.7912 | 0.103 |
| 4 | 0.6327 | 0.5946 | 0.1135 | 0.4255 | 0.7521 | 0.7719 | 0.7726 | 0.103 |
| 5 | 0.6216 | 0.5832 | 0.1135 | 0.423 | 0.725 | 0.7506 | 0.753 | 0.103 |
| 6 | 0.6149 | 0.5754 | 0.1135 | 0.4221 | 0.6976 | 0.725 | 0.7286 | 0.103 |
| 7 | 0.6099 | 0.5692 | 0.1135 | 0.4219 | 0.6723 | 0.7024 | 0.7043 | 0.103 |
| Over_dim_tied_iteration 256 10_Targets | Over_dim_tied_iteration 128 10_Targets | Over_dim_tied_iteration 64 10_Targets | Over_dim_tied_iteration 32 10_Targets | Over_dim_tied_iteration 256 Mnist | Over_dim_tied_iteration 128 Mnist | Over_dim_tied_iteration 64 Mnist | Over_dim_tied_iteration 32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.348702 | 0.330399 | 0.455832 | 0.283863 | 2.43769e+08 | 3.132e+09 | 2.75913e+09 | 0.421153 |
| 1 | 0.380188 | 0.370205 | 0.455832 | 0.348805 | 1.14069e+24 | 1.56844e+26 | 1.65347e+26 | 21.5301 |
| 2 | 0.397133 | 0.399206 | 0.455832 | 0.398787 | inf | inf | inf | 21.5304 |
| 3 | 0.405673 | 0.423216 | 0.455832 | 0.426219 | inf | inf | inf | 21.5305 |
| 4 | 0.410899 | 0.442688 | 0.455832 | 0.440156 | inf | nan | nan | 21.5305 |
| 5 | 0.414661 | 0.455425 | 0.455832 | 0.447267 | nan | nan | nan | 21.5305 |
| 6 | 0.417293 | 0.462817 | 0.455832 | 0.45064 | nan | nan | nan | 21.5305 |
| 7 | 0.419109 | 0.467238 | 0.455832 | 0.45208 | nan | nan | nan | 21.5305 |
| Over_dim_tied_iteration 256 10_Targets | Over_dim_tied_iteration 128 10_Targets | Over_dim_tied_iteration 64 10_Targets | Over_dim_tied_iteration 32 10_Targets | Over_dim_tied_iteration 256 Mnist | Over_dim_tied_iteration 128 Mnist | Over_dim_tied_iteration 64 Mnist | Over_dim_tied_iteration 32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.260999 | 0.266667 | 0.265029 | 0.262074 | 935.544 | 4908.02 | 4751.9 | 0.266758 |
| 1 | 0.265838 | 0.275847 | 0.265029 | 0.271626 | 6.39918e+10 | 1.09825e+12 | 1.16324e+12 | 0.353396 |
| 2 | 0.268396 | 0.284265 | 0.265029 | 0.281432 | 4.37744e+18 | 2.45767e+20 | 2.84762e+20 | 0.353661 |
| 3 | 0.269674 | 0.29115 | 0.265029 | 0.287471 | 2.99444e+26 | 5.4998e+28 | 6.971e+28 | 0.353746 |
| 4 | 0.270451 | 0.296502 | 0.265029 | 0.290606 | inf | nan | nan | 0.353769 |
| 5 | 0.271091 | 0.300013 | 0.265029 | 0.292153 | nan | nan | nan | 0.353774 |
| 6 | 0.271571 | 0.302154 | 0.265029 | 0.292831 | nan | nan | nan | 0.353776 |
| 7 | 0.271871 | 0.303532 | 0.265029 | 0.293116 | nan | nan | nan | 0.353776 |
predictions_df_60
| Over_dim_tied_iteration 256 10_Targets | Over_dim_tied_iteration 128 10_Targets | Over_dim_tied_iteration 64 10_Targets | Over_dim_tied_iteration 32 10_Targets | Over_dim_tied_iteration 256 Mnist | Over_dim_tied_iteration 128 Mnist | Over_dim_tied_iteration 64 Mnist | Over_dim_tied_iteration 32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.7723 | 0.7634 | 0.1726 | 0.5805 | 0.704 | 0.6813 | 0.7056 | 0.1183 |
| 1 | 0.6829 | 0.6588 | 0.1135 | 0.4729 | 0.7283 | 0.7225 | 0.7424 | 0.1054 |
| 2 | 0.6249 | 0.5971 | 0.1135 | 0.4284 | 0.7214 | 0.7306 | 0.7448 | 0.103 |
| 3 | 0.5986 | 0.5651 | 0.1135 | 0.4084 | 0.7055 | 0.7207 | 0.7289 | 0.103 |
| 4 | 0.5853 | 0.5455 | 0.1135 | 0.4027 | 0.6841 | 0.7028 | 0.7077 | 0.103 |
| 5 | 0.5729 | 0.5351 | 0.1135 | 0.4003 | 0.6588 | 0.6822 | 0.6862 | 0.103 |
| 6 | 0.566 | 0.5283 | 0.1135 | 0.3992 | 0.6302 | 0.66 | 0.666 | 0.103 |
| 7 | 0.563 | 0.5242 | 0.1135 | 0.3992 | 0.605 | 0.6353 | 0.6422 | 0.103 |
| Over_dim_tied_iteration 256 10_Targets | Over_dim_tied_iteration 128 10_Targets | Over_dim_tied_iteration 64 10_Targets | Over_dim_tied_iteration 32 10_Targets | Over_dim_tied_iteration 256 Mnist | Over_dim_tied_iteration 128 Mnist | Over_dim_tied_iteration 64 Mnist | Over_dim_tied_iteration 32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.346268 | 0.330528 | 0.45586 | 0.285116 | 4.10346e+08 | 3.7645e+09 | 3.35784e+09 | 0.639216 |
| 1 | 0.38144 | 0.374805 | 0.455832 | 0.354166 | 1.92018e+24 | 1.88518e+26 | 2.01226e+26 | 55.375 |
| 2 | 0.399868 | 0.406582 | 0.455832 | 0.40825 | inf | inf | inf | 55.3751 |
| 3 | 0.40871 | 0.433265 | 0.455832 | 0.437251 | inf | inf | inf | 55.3752 |
| 4 | 0.414274 | 0.454806 | 0.455832 | 0.45187 | inf | nan | nan | 55.3752 |
| 5 | 0.418373 | 0.468483 | 0.455832 | 0.458995 | nan | nan | nan | 55.3752 |
| 6 | 0.421099 | 0.476399 | 0.455832 | 0.462409 | nan | nan | nan | 55.3752 |
| 7 | 0.422988 | 0.480929 | 0.455832 | 0.463859 | nan | nan | nan | 55.3752 |
| Over_dim_tied_iteration 256 10_Targets | Over_dim_tied_iteration 128 10_Targets | Over_dim_tied_iteration 64 10_Targets | Over_dim_tied_iteration 32 10_Targets | Over_dim_tied_iteration 256 Mnist | Over_dim_tied_iteration 128 Mnist | Over_dim_tied_iteration 64 Mnist | Over_dim_tied_iteration 32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.263403 | 0.270365 | 0.265031 | 0.266306 | 1690.16 | 5984.37 | 5459.51 | 0.277732 |
| 1 | 0.267936 | 0.280857 | 0.265029 | 0.276591 | 1.15614e+11 | 1.3391e+12 | 1.33646e+12 | 0.495049 |
| 2 | 0.270106 | 0.290024 | 0.265029 | 0.287057 | 7.90869e+18 | 2.99665e+20 | 3.27167e+20 | 0.495305 |
| 3 | 0.271326 | 0.297545 | 0.265029 | 0.293343 | 5.41004e+26 | 6.70594e+28 | 8.00907e+28 | 0.495398 |
| 4 | 0.272211 | 0.303374 | 0.265029 | 0.296598 | inf | nan | nan | 0.495424 |
| 5 | 0.272842 | 0.3071 | 0.265029 | 0.298169 | nan | nan | nan | 0.49543 |
| 6 | 0.273261 | 0.309381 | 0.265029 | 0.298891 | nan | nan | nan | 0.495432 |
| 7 | 0.273606 | 0.310766 | 0.265029 | 0.299172 | nan | nan | nan | 0.495432 |
predictions_df_70
| Over_dim_tied_iteration 256 10_Targets | Over_dim_tied_iteration 128 10_Targets | Over_dim_tied_iteration 64 10_Targets | Over_dim_tied_iteration 32 10_Targets | Over_dim_tied_iteration 256 Mnist | Over_dim_tied_iteration 128 Mnist | Over_dim_tied_iteration 64 Mnist | Over_dim_tied_iteration 32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.6872 | 0.6905 | 0.166 | 0.5129 | 0.6275 | 0.5978 | 0.6352 | 0.1025 |
| 1 | 0.6225 | 0.5899 | 0.1135 | 0.4408 | 0.6494 | 0.6308 | 0.6627 | 0.1087 |
| 2 | 0.5732 | 0.5301 | 0.1135 | 0.3958 | 0.6443 | 0.6384 | 0.6618 | 0.1034 |
| 3 | 0.5519 | 0.5007 | 0.1135 | 0.3793 | 0.6244 | 0.6274 | 0.6496 | 0.1034 |
| 4 | 0.5386 | 0.4835 | 0.1135 | 0.3733 | 0.6025 | 0.6074 | 0.6339 | 0.1033 |
| 5 | 0.528 | 0.4741 | 0.1135 | 0.3714 | 0.5837 | 0.5872 | 0.6141 | 0.1033 |
| 6 | 0.5216 | 0.4688 | 0.1135 | 0.3707 | 0.5582 | 0.5692 | 0.5935 | 0.1033 |
| 7 | 0.5175 | 0.4647 | 0.1135 | 0.3702 | 0.5384 | 0.5521 | 0.5733 | 0.1033 |
| Over_dim_tied_iteration 256 10_Targets | Over_dim_tied_iteration 128 10_Targets | Over_dim_tied_iteration 64 10_Targets | Over_dim_tied_iteration 32 10_Targets | Over_dim_tied_iteration 256 Mnist | Over_dim_tied_iteration 128 Mnist | Over_dim_tied_iteration 64 Mnist | Over_dim_tied_iteration 32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.345311 | 0.331163 | 0.455879 | 0.287811 | 1.10533e+09 | 8.74675e+09 | 5.97337e+09 | 1.68987 |
| 1 | 0.38267 | 0.381589 | 0.455832 | 0.361305 | 5.1723e+24 | 4.38018e+26 | 3.57968e+26 | 178.328 |
| 2 | 0.401484 | 0.417522 | 0.455832 | 0.419539 | inf | inf | inf | 178.066 |
| 3 | 0.410695 | 0.445576 | 0.455832 | 0.450089 | inf | inf | inf | 178.066 |
| 4 | 0.416689 | 0.467899 | 0.455832 | 0.464693 | inf | nan | nan | 178.066 |
| 5 | 0.421133 | 0.482281 | 0.455832 | 0.471655 | nan | nan | nan | 178.066 |
| 6 | 0.424612 | 0.490507 | 0.455832 | 0.475162 | nan | nan | nan | 178.066 |
| 7 | 0.426578 | 0.494963 | 0.455832 | 0.476928 | nan | nan | nan | 178.066 |
| Over_dim_tied_iteration 256 10_Targets | Over_dim_tied_iteration 128 10_Targets | Over_dim_tied_iteration 64 10_Targets | Over_dim_tied_iteration 32 10_Targets | Over_dim_tied_iteration 256 Mnist | Over_dim_tied_iteration 128 Mnist | Over_dim_tied_iteration 64 Mnist | Over_dim_tied_iteration 32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.266741 | 0.275313 | 0.265031 | 0.271139 | 3621.85 | 11787.1 | 8252.29 | 0.310971 |
| 1 | 0.270541 | 0.28689 | 0.265029 | 0.282079 | 2.47758e+11 | 2.63761e+12 | 2.02013e+12 | 1.0094 |
| 2 | 0.272201 | 0.296816 | 0.265029 | 0.293328 | 1.69482e+19 | 5.90247e+20 | 4.9453e+20 | 1.00902 |
| 3 | 0.273173 | 0.304589 | 0.265029 | 0.300047 | 1.15936e+27 | 1.32086e+29 | 1.21061e+29 | 1.0091 |
| 4 | 0.274019 | 0.310635 | 0.265029 | 0.303301 | inf | nan | nan | 1.00913 |
| 5 | 0.274695 | 0.314567 | 0.265029 | 0.304806 | nan | nan | nan | 1.00913 |
| 6 | 0.275201 | 0.316937 | 0.265029 | 0.30553 | nan | nan | nan | 1.00914 |
| 7 | 0.275476 | 0.318265 | 0.265029 | 0.305872 | nan | nan | nan | 1.00914 |
predictions_df_80
| Over_dim_tied_iteration 256 10_Targets | Over_dim_tied_iteration 128 10_Targets | Over_dim_tied_iteration 64 10_Targets | Over_dim_tied_iteration 32 10_Targets | Over_dim_tied_iteration 256 Mnist | Over_dim_tied_iteration 128 Mnist | Over_dim_tied_iteration 64 Mnist | Over_dim_tied_iteration 32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.5941 | 0.614 | 0.1561 | 0.4474 | 0.5578 | 0.5126 | 0.554 | 0.0982 |
| 1 | 0.5529 | 0.5198 | 0.1135 | 0.3905 | 0.5777 | 0.5473 | 0.5822 | 0.1093 |
| 2 | 0.5116 | 0.4669 | 0.1135 | 0.3597 | 0.5676 | 0.5533 | 0.5822 | 0.1034 |
| 3 | 0.4933 | 0.4402 | 0.1135 | 0.3448 | 0.5536 | 0.5459 | 0.5685 | 0.1029 |
| 4 | 0.4811 | 0.4262 | 0.1135 | 0.3407 | 0.5305 | 0.5323 | 0.5544 | 0.1029 |
| 5 | 0.4696 | 0.4178 | 0.1135 | 0.3387 | 0.5105 | 0.509 | 0.535 | 0.1029 |
| 6 | 0.4641 | 0.4135 | 0.1135 | 0.3386 | 0.49 | 0.4912 | 0.5162 | 0.1029 |
| 7 | 0.4607 | 0.4103 | 0.1135 | 0.3386 | 0.4721 | 0.4758 | 0.4979 | 0.1029 |
| Over_dim_tied_iteration 256 10_Targets | Over_dim_tied_iteration 128 10_Targets | Over_dim_tied_iteration 64 10_Targets | Over_dim_tied_iteration 32 10_Targets | Over_dim_tied_iteration 256 Mnist | Over_dim_tied_iteration 128 Mnist | Over_dim_tied_iteration 64 Mnist | Over_dim_tied_iteration 32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.346407 | 0.334849 | 0.457756 | 0.293611 | 1.93055e+09 | 1.47257e+10 | 8.29947e+09 | 12.6991 |
| 1 | 0.386937 | 0.390018 | 0.455832 | 0.374307 | 9.03382e+24 | 7.37431e+26 | 4.97365e+26 | 358.385 |
| 2 | 0.407927 | 0.43037 | 0.455832 | 0.43774 | inf | inf | inf | 355.811 |
| 3 | 0.417908 | 0.462715 | 0.455832 | 0.469107 | inf | inf | inf | 355.811 |
| 4 | 0.424006 | 0.486917 | 0.455832 | 0.483079 | inf | nan | nan | 355.811 |
| 5 | 0.428975 | 0.500427 | 0.455832 | 0.489804 | nan | nan | nan | 355.811 |
| 6 | 0.432477 | 0.50766 | 0.455832 | 0.493094 | nan | nan | nan | 355.811 |
| 7 | 0.434504 | 0.511629 | 0.455832 | 0.494609 | nan | nan | nan | 355.811 |
| Over_dim_tied_iteration 256 10_Targets | Over_dim_tied_iteration 128 10_Targets | Over_dim_tied_iteration 64 10_Targets | Over_dim_tied_iteration 32 10_Targets | Over_dim_tied_iteration 256 Mnist | Over_dim_tied_iteration 128 Mnist | Over_dim_tied_iteration 64 Mnist | Over_dim_tied_iteration 32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.272003 | 0.282449 | 0.265065 | 0.277778 | 5620 | 16731.4 | 10493 | 0.415634 |
| 1 | 0.275256 | 0.295306 | 0.265029 | 0.290361 | 3.84448e+11 | 3.74402e+12 | 2.56867e+12 | 1.75825 |
| 2 | 0.276552 | 0.305941 | 0.265029 | 0.30262 | 2.62987e+19 | 8.37841e+20 | 6.28812e+20 | 1.75362 |
| 3 | 0.277333 | 0.314389 | 0.265029 | 0.309511 | 1.79899e+27 | 1.87493e+29 | 1.53934e+29 | 1.75363 |
| 4 | 0.278053 | 0.320522 | 0.265029 | 0.312644 | inf | nan | nan | 1.75364 |
| 5 | 0.278747 | 0.32402 | 0.265029 | 0.314089 | nan | nan | nan | 1.75364 |
| 6 | 0.279206 | 0.326061 | 0.265029 | 0.31474 | nan | nan | nan | 1.75365 |
| 7 | 0.279488 | 0.327278 | 0.265029 | 0.315006 | nan | nan | nan | 1.75365 |
predictions_df_90
| Over_dim_tied_iteration 256 10_Targets | Over_dim_tied_iteration 128 10_Targets | Over_dim_tied_iteration 64 10_Targets | Over_dim_tied_iteration 32 10_Targets | Over_dim_tied_iteration 256 Mnist | Over_dim_tied_iteration 128 Mnist | Over_dim_tied_iteration 64 Mnist | Over_dim_tied_iteration 32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.4997 | 0.529 | 0.144 | 0.3873 | 0.4706 | 0.4362 | 0.4696 | 0.0932 |
| 1 | 0.4694 | 0.4499 | 0.1135 | 0.3341 | 0.4855 | 0.4583 | 0.4932 | 0.1127 |
| 2 | 0.4377 | 0.4 | 0.1135 | 0.3112 | 0.4775 | 0.4567 | 0.4906 | 0.1054 |
| 3 | 0.4262 | 0.3725 | 0.1135 | 0.2996 | 0.4645 | 0.4456 | 0.4802 | 0.1028 |
| 4 | 0.4175 | 0.3611 | 0.1135 | 0.297 | 0.447 | 0.4349 | 0.464 | 0.1029 |
| 5 | 0.4102 | 0.3548 | 0.1135 | 0.2954 | 0.434 | 0.4211 | 0.4474 | 0.1029 |
| 6 | 0.4049 | 0.3519 | 0.1135 | 0.295 | 0.4165 | 0.4107 | 0.4343 | 0.1029 |
| 7 | 0.4006 | 0.3499 | 0.1135 | 0.2948 | 0.3967 | 0.3959 | 0.4222 | 0.1029 |
| Over_dim_tied_iteration 256 10_Targets | Over_dim_tied_iteration 128 10_Targets | Over_dim_tied_iteration 64 10_Targets | Over_dim_tied_iteration 32 10_Targets | Over_dim_tied_iteration 256 Mnist | Over_dim_tied_iteration 128 Mnist | Over_dim_tied_iteration 64 Mnist | Over_dim_tied_iteration 32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.350723 | 0.33853 | 0.458633 | 0.300344 | 2.7256e+09 | 1.93238e+10 | 9.3675e+09 | 31.7281 |
| 1 | 0.393889 | 0.398521 | 0.455832 | 0.388426 | 1.27542e+25 | 9.67693e+26 | 5.6137e+26 | 848.224 |
| 2 | 0.415655 | 0.443372 | 0.455832 | 0.457853 | inf | inf | inf | 850.991 |
| 3 | 0.425754 | 0.478088 | 0.455832 | 0.490306 | inf | inf | inf | 850.99 |
| 4 | 0.431976 | 0.503624 | 0.455832 | 0.504277 | inf | nan | nan | 850.99 |
| 5 | 0.436801 | 0.517716 | 0.455832 | 0.511011 | nan | nan | nan | 850.99 |
| 6 | 0.440411 | 0.524611 | 0.455832 | 0.514405 | nan | nan | nan | 850.99 |
| 7 | 0.442905 | 0.528114 | 0.455832 | 0.516103 | nan | nan | nan | 850.99 |
| Over_dim_tied_iteration 256 10_Targets | Over_dim_tied_iteration 128 10_Targets | Over_dim_tied_iteration 64 10_Targets | Over_dim_tied_iteration 32 10_Targets | Over_dim_tied_iteration 256 Mnist | Over_dim_tied_iteration 128 Mnist | Over_dim_tied_iteration 64 Mnist | Over_dim_tied_iteration 32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.27714 | 0.288954 | 0.265105 | 0.284686 | 8250.8 | 22807.6 | 11522.1 | 0.641987 |
| 1 | 0.279903 | 0.302578 | 0.265029 | 0.299639 | 5.6442e+11 | 5.10372e+12 | 2.82059e+12 | 3.82172 |
| 2 | 0.280838 | 0.314086 | 0.265029 | 0.313184 | 3.86098e+19 | 1.14211e+21 | 6.90482e+20 | 3.82823 |
| 3 | 0.281534 | 0.322985 | 0.265029 | 0.320241 | 2.64116e+27 | 2.55583e+29 | 1.6903e+29 | 3.82814 |
| 4 | 0.28225 | 0.329345 | 0.265029 | 0.323402 | inf | nan | nan | 3.82813 |
| 5 | 0.282941 | 0.332916 | 0.265029 | 0.324882 | nan | nan | nan | 3.82813 |
| 6 | 0.283448 | 0.334816 | 0.265029 | 0.325589 | nan | nan | nan | 3.82813 |
| 7 | 0.283824 | 0.335815 | 0.265029 | 0.325928 | nan | nan | nan | 3.82813 |
predictions_df_100
| Over_dim_tied_iteration 256 10_Targets | Over_dim_tied_iteration 128 10_Targets | Over_dim_tied_iteration 64 10_Targets | Over_dim_tied_iteration 32 10_Targets | Over_dim_tied_iteration 256 Mnist | Over_dim_tied_iteration 128 Mnist | Over_dim_tied_iteration 64 Mnist | Over_dim_tied_iteration 32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.4116 | 0.4452 | 0.1381 | 0.3281 | 0.3905 | 0.3498 | 0.3891 | 0.0947 |
| 1 | 0.3988 | 0.3903 | 0.113 | 0.278 | 0.3982 | 0.3637 | 0.404 | 0.1161 |
| 2 | 0.3788 | 0.3427 | 0.1135 | 0.2634 | 0.3874 | 0.3634 | 0.4019 | 0.1066 |
| 3 | 0.3686 | 0.3217 | 0.1135 | 0.2547 | 0.3769 | 0.3609 | 0.3973 | 0.1017 |
| 4 | 0.3604 | 0.3119 | 0.1135 | 0.2507 | 0.3602 | 0.3496 | 0.3848 | 0.1014 |
| 5 | 0.3543 | 0.3067 | 0.1135 | 0.2504 | 0.3508 | 0.3403 | 0.3759 | 0.1014 |
| 6 | 0.3494 | 0.3035 | 0.1135 | 0.2503 | 0.3332 | 0.3299 | 0.3654 | 0.1014 |
| 7 | 0.345 | 0.3017 | 0.1135 | 0.2502 | 0.3235 | 0.3182 | 0.3541 | 0.1014 |
| Over_dim_tied_iteration 256 10_Targets | Over_dim_tied_iteration 128 10_Targets | Over_dim_tied_iteration 64 10_Targets | Over_dim_tied_iteration 32 10_Targets | Over_dim_tied_iteration 256 Mnist | Over_dim_tied_iteration 128 Mnist | Over_dim_tied_iteration 64 Mnist | Over_dim_tied_iteration 32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.356122 | 0.347002 | 0.462827 | 0.311531 | 5.14684e+09 | 3.58009e+10 | 1.45927e+10 | 63.5763 |
| 1 | 0.400606 | 0.412539 | 0.455832 | 0.409514 | 2.40842e+25 | 1.79283e+27 | 8.74503e+26 | 1308.47 |
| 2 | 0.423798 | 0.459436 | 0.455832 | 0.484753 | inf | inf | inf | 1318.89 |
| 3 | 0.434797 | 0.494455 | 0.455832 | 0.51849 | inf | inf | inf | 1320.79 |
| 4 | 0.441665 | 0.518924 | 0.455832 | 0.532509 | inf | nan | nan | 1320.79 |
| 5 | 0.446666 | 0.532304 | 0.455832 | 0.538773 | nan | nan | nan | 1320.79 |
| 6 | 0.450795 | 0.538465 | 0.455832 | 0.541786 | nan | nan | nan | 1320.79 |
| 7 | 0.453646 | 0.541711 | 0.455832 | 0.543212 | nan | nan | nan | 1320.79 |
| Over_dim_tied_iteration 256 10_Targets | Over_dim_tied_iteration 128 10_Targets | Over_dim_tied_iteration 64 10_Targets | Over_dim_tied_iteration 32 10_Targets | Over_dim_tied_iteration 256 Mnist | Over_dim_tied_iteration 128 Mnist | Over_dim_tied_iteration 64 Mnist | Over_dim_tied_iteration 32 Mnist | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.284071 | 0.296984 | 0.265219 | 0.293099 | 15140.9 | 40761.3 | 19023.8 | 1.0109 |
| 1 | 0.286092 | 0.311423 | 0.265029 | 0.311138 | 1.03576e+12 | 9.12133e+12 | 4.65701e+12 | 5.74944 |
| 2 | 0.286653 | 0.322865 | 0.265029 | 0.32649 | 7.08525e+19 | 2.04118e+21 | 1.14004e+21 | 5.7915 |
| 3 | 0.287124 | 0.331344 | 0.265029 | 0.333993 | 4.84676e+27 | 4.56777e+29 | 2.79083e+29 | 5.79657 |
| 4 | 0.287837 | 0.337172 | 0.265029 | 0.33716 | inf | nan | nan | 5.79653 |
| 5 | 0.288366 | 0.340448 | 0.265029 | 0.338538 | nan | nan | nan | 5.79652 |
| 6 | 0.288888 | 0.342092 | 0.265029 | 0.339146 | nan | nan | nan | 5.79652 |
| 7 | 0.289341 | 0.343004 | 0.265029 | 0.339424 | nan | nan | nan | 5.79652 |
/home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:397: UserWarning: Warning: converting a masked element to nan. dv = (np.float64(self.norm.vmax) - /home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:398: UserWarning: Warning: converting a masked element to nan. np.float64(self.norm.vmin)) /home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:405: UserWarning: Warning: converting a masked element to nan. a_min = np.float64(newmin) /home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:410: UserWarning: Warning: converting a masked element to nan. a_max = np.float64(newmax) <string>:6: UserWarning: Warning: converting a masked element to nan. /home/hicky/anaconda3/lib/python3.7/site-packages/numpy/ma/core.py:711: UserWarning: Warning: converting a masked element to nan. data = np.array(a, copy=False, subok=subok)
/home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:397: UserWarning: Warning: converting a masked element to nan. dv = (np.float64(self.norm.vmax) - /home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:398: UserWarning: Warning: converting a masked element to nan. np.float64(self.norm.vmin)) /home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:405: UserWarning: Warning: converting a masked element to nan. a_min = np.float64(newmin) /home/hicky/anaconda3/lib/python3.7/site-packages/matplotlib/image.py:410: UserWarning: Warning: converting a masked element to nan. a_max = np.float64(newmax) <string>:6: UserWarning: Warning: converting a masked element to nan. /home/hicky/anaconda3/lib/python3.7/site-packages/numpy/ma/core.py:711: UserWarning: Warning: converting a masked element to nan. data = np.array(a, copy=False, subok=subok)